Accident monitoring using remotely operated or autonomous aerial vehicles

ABSTRACT

A system to monitor vehicle accidents using a network of aerial based monitoring systems, terrestrial based monitoring systems and in-vehicle monitoring systems is described. Aerial vehicles used for this surveillance include manned and unmanned aircraft, satellites and lighter than air craft. Aerial vehicles can also be deployed from vehicles. The deployment is triggered by sensors registering a pattern in the data that is indicative of an accident that has happened or an accident about to happen.

1 RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application62/109,434 filed on 29 Jan. 2015 and entitled ACCIDENT MONITORING USINGREMOTELY OPERATED OR AUTONOMOUS AERIAL VEHICLES which is hereinincorporated by reference.

U.S. patent application titled “SYSTEM AND METHOD FOR USE OF PATTERNRECOGNITION IN ASSESSING OR MONITORING VEHICLE STATUS OR OPERATORDRIVING BEHAVIOR”, application Ser. No. 13/679,722, filed Nov. 16, 2012;which claims the benefit of priority to U.S. Provisional PatentApplication No. 61/578,511, filed Dec. 21, 2011; PCT/US12/71487 titled“SYSTEMS AND METHODS FOR ASSESSING OR MONITORING VEHICLE STATUS OROPERATOR STATUS” filed 21 Dec. 2012; and Ser. No. 14/317,624 titled“System and method for Determining Of Vehicle Accident information” fileon 27 Jun. 2014; each of which the above applications are hereinincorporated by reference.

2 INTRODUCTION

Aspects of this invention are designed to provide as close to real-timesurveillance of a vehicle accident as possible to both estimate theamount and extent of damage to the vehicle or vehicles and to determinebodily harm. In response to the estimates appropriate emergency responsevehicles can be deployed and the repair process can be initiatedincluding relaying, to adjusters, damage estimates and surveillanceinformation to determine causality.

Specific aspects of this invention include methods and system to detectaccidents before they happen or while they happen; methods and systemsto anticipate an accident based on measurements acquired of vehiclemovements and driving conditions that are historically indicative of anaccident.

Sensors within a vehicle; deployed from a vehicle for aerialsurveillance; deployed from a fixed based station for aerialsurveillance; long term flight aerial surveillance; and fixed sensorsthat monitor a transportation network may be deployed.

Various techniques for image analysis, statistics and machine learningare utilized to analyze both real-time and historical data concerningaccidents.

In addition to accident detection and analysis the systems and methodsdescribed within this document are also useful, for example, for trafficmonitoring, and crime prevention and detection.

3 BACKGROUND

Driving a vehicle continues to be one of the most hazardous activitiesthat a person can participate in. Vehicle accidents are one of theleading cause of death every year. Damage from accidents amounts tobillions of dollars a year. To date, most vehicle accidents are assessedafter-the-fact by personal arriving on the scene after the incident. Theassessments almost always utilize some type of human interface either toestimate damage or transpose information into a machine readable form.This human interface introduces many biases and uncertainties into theprocess. These biases and uncertainties then translate into litigationwhen it is necessary to determine cause for insurance purposes or from asafety standard.

Attempts have been made to take sensor information from in-vehiclesensors and associate this information to external information andfactors to reconstruct accidents and/or determine when an accidentoccurs, but this work is in early stages.

The advent of unmanned aerial vehicles (UAV) with associated sensorarrays has added a new method of monitoring vehicle activity andaccident scene surveillance. However, the changing regulatory atmospheremakes reliance on any one type of surveillance method risky from thefact that it may be illegal in the near future. For example, in December2014, the US Congress is considering that UAVs for commercial unlicenseduse can only fly 400 feet in the air and must be in view of the handler.Even more restrictive rulings may apply. For this reason and others,data fusion among several sensor arrays is important to any vehicle oraccident scene surveillance system as certain methods may not be allowedin the long run.

An aim of this invention is both to navigate the uncertain regulatorylandscape and also take advantage of the array of sensors; sensordelivery vehicles and methods; and statistical and machine learninganalysis techniques for accident prediction and accident scenesurveillance.

4 BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 depicts and embodiment using aerial surveillance from a blimp.

FIG. 2 Depicts an accident scene being tracked.

FIG. 3 is a flowchart showing input into an Accident Prediction Module

FIG. 4 is a prior art depiction of rocket propelled surveillance system.

FIG. 5 is a prior art depiction of a small quad copter with mountedcamera.

FIG. 6 is a flowchart of events required to launch an aerialreconnaissance device from a vehicle.

5 OVERVIEW

Some objects of this invention are systems and methods to detect vehicleaccidents and observe vehicle accident scenes.

The tools used for this are sensors within a vehicle or vehiclesincluding: video cameras; sensors that are part of the vehicle; andadditional sensors that are part of a portable device within thevehicle. Other sensor systems include stationary sensors that areassociated with the vehicle transportation network, for example, trafficcounters, and speed cameras. Additional information may be provided byweather stations. Aerial sensors can be mounted, for example, in ROVS,autonomous drones, and manned aircraft. In addition, sensors can beoutside the atmosphere mounted on satellites. Individual vehicles may betracked by GPS or wireless transmitter signal strength triangulation toassess movements prior to an accident.

Analysis consists of statistical analysis of sensor data from one ormore system types and delivery systems where the analysis is performedby comparison of historical patterns indicative of an accident about tooccur or an accident that has happened and further patterns used toassess damage and injury.

Novel ROV/autonomous flying vehicles that are deployed from a vehicleare also part of this invention.

5.1 Surveillance Methods

Surveillance methods for this document are broken into five types:

-   -   Aerial surveillance which has an identified area to monitor        vehicle movement and activities. The area could be part of a        road network defined by geographic borders; it could be an        intersection known to have a potential for many collisions. The        area could change during different time periods or day of the        week based on historical collision or accident rates.    -   On-Vehicle Surveillance which consists of a sensor suite that is        part of the vehicle and perhaps sensors that are part of a        mobile device within the vehicle.    -   Event Surveillance where sensor suites are deployed (usually in        the air) from either a vehicle, a fixed base station or where a        passive sensors system is trained on an accident scene once an        accident or potential accident is detected.    -   Ground Based surveillance which typically is performed by        stationary sensors along a roadway or intersection and measures        things like traffic counts or vehicle speed (an average speed)        and/or weather and road conditions.    -   Remote Sensing consists of numerous techniques including such        things as weather satellites that can provide background        information with respect to weather and road conditions.

All of these surveillance method could be used in both a passive oractive mode. Passive mode is where general information is recorded andstored for a fixed amount of time, then discarded unless an event suchas an accident is identified. If an event such as an accident occurs,pertinent information is retrieved and analyzed and then transmitted toan analysis station or first responders or other surveillance systems.

Active mode surveillance is defined as occurring when some sensorpattern indicative of an event of interest occurs and is used toinitiate specific surveillance. The sensor pattern may triggeradditional recordation of information and/or direct sensors to monitorat a certain location and perhaps with an increased frequency ofmeasurement than that which happens during passive surveillance.

5.1.1 Aerial Surveillance

If is known in the art how to identify and track multiple vehiclesand/or pedestrians using high altitude surveillance. This technology ismuch like facial recognition software used in virtually every digitalcamera where multiple faces can be identified and tracked. AerialSurveillance can be from fixed wing aircraft or rotary aircraft orlighter than air vehicles. The surveillance can occur from manned orunmanned vehicles.

In an embodiment of this invention passive aerial surveillance is usedby itself or in tandem with other surveillance methods.

An example of a passive aerial surveillance is shown in FIG. 1. Anaerial vehicle 101 continually scans an area filled with roads andvehicles 102. Vehicles coming in and out of the area 102 are identified.An account of individual vehicles entering and leaving the survey areacan be maintained over time.

An aerial surveillance module (either that is part of the aerialsurveillance vehicle or that is in remote communication with the aerialvehicle) is used to observe ground vehicle movement.

The aerial surveillance module, in addition to vehicle recognitionsoftware, also has a digital map of the survey area. By tracking themovement of individual vehicles through the survey area, the aerialsurveillance module can detect:

-   -   If a vehicle is off the road    -   If a vehicle is exceeding the speed limit    -   If a vehicle is in very close proximity with another vehicle    -   If equipped with thermal imaging, if a vehicle is on fire.    -   A vehicle driving erratically or swerving    -   Traffic as a whole moving slowly or stopped

During analysis of the passively acquired data, the aerial surveillancemodule can transmit instructions to other surveillance systems (eitheraerial, fixed or vehicle based) via wireless communications to alertthese other systems that active monitoring of a situation may benecessary.

FIG. 2 depicts result that could be obtained from aerial surveillance.At a time 1, vehicle 201 and a second vehicle 203 are observed at afirst location and are continued to be tracked until a second time whereit is observed that Vehicle 203 collides with vehicle 201 at location205. Upon detection of the accident pattern indicative of the collision(described elsewhere in this text), information may be transmitted tovehicles 201 and 203 or to emergency authorities or others. Theinformation may contain the travel history of the two vehicles includingtheir locations and speeds and driving behavior.

In addition to the above triggers, communications may be initiated withother surveillance systems when a vehicle moves out of the surveillancearea and if there was a reason to continue monitoring it in otherquadrants or surveillance areas.

In an embodiment, a scenario for aerial surveillance is:

-   -   1) Identify vehicles entering the surveillance area    -   2) Track each vehicle and determine speed and acceleration        through the area    -   3) Communicate with weather and on-vehicle surveillance systems        and identify risky driving behavior patterns associated with the        current driving conditions.    -   4) Identify vehicles exhibiting risky driving behavior in the        surveillance area by comparison of movements of each vehicle        relative to the road network and speed limits and appropriate        driving behavior patterns for the driving conditions.    -   5) Do one or more of the following:        -   a. Notify local surveillance assets to start actively            monitoring vehicles with risky behavior by transmitting            location and trajectory information        -   b. Notify individual vehicle monitoring systems in the            vicinity of vehicles that are driving in a risky manner, of            the risk, and make sure that monitoring systems are            activated        -   c. When vehicles are near the boundary of the aerial            surveillance area, notify the adjacent aerial surveillance            areas to actively monitor the incoming vehicle.    -   6) Store the above information for a specified period of time        until such time as no accident is detected.    -   7) If an accident is predicted to happen, for example, due to        proximity warnings from aerial surveillance, identify the        vehicles involved and transmit the stored information of the        driving path of the vehicles involved to the on-vehicle        monitoring systems, the insurance company and/or the authorities        or emergency responders.

Aerial surveillance at lower altitudes may comprise passive monitoring,for example, at a busy intersection where many accidents are known tohappen simply scan the intersection recording a time series ofinformation (for example video) and simultaneously be performing patternrecognition analysis on the information for patterns that would indicatean accident or impending accident. Once an accident is detected or isimminent, the time series data that is pertinent to the accident, istransferred to an analysis station or the authorities or to vehiclesinvolved in the accident.

Surveillance system at an interchange, for example, may not be on an airvehicle, but could be attached to a pole or other structure wheresensors are high above the interchange, so effectively there is anaerial view of the interchange.

Active surveillance may be initiated when any passive surveillancesystem detects a pattern of concern. Active surveillance would occurwhen a passive surveillance system deviated from it standard sweep pathto monitor a specific vehicle or vehicles or a specific location.

5.1.2 On-Vehicle Surveillance

Vehicles equipped with sensors that measure vehicle motion, and vehiclebehavior and/or motion and behavior of adjacent vehicles fall into thiscategory and are part of embodiments of this invention.

On-vehicle sensors are monitored for patterns indicative of an accidentoccurrence or an impending accident. These patterns, for example, couldbe rapid changes in acceleration, proximity alerts either from videoanalysis or other electromagnetic monitoring such as sonar, or infrared.

5.1.3 Event Surveillance

Once an accident pattern is detected by any of one of the surveillancesystems, then, provided the various system are in communication, thesystem that detects a pattern transmits the information about thepattern (when and where and what) and requests that other systems bedeployed and/or focused on the event of interest.

5.1.4 Ground Based Surveillance

Ground based surveillance can be one of:

-   -   Sensors embedded in pavement that detect things such as traffic        counts or average speed of vehicles;    -   Localized weather conditions    -   Ice or other covering of the road surface    -   Images of traffic passing past a fixed position.

Any of the above information can be included in an accident pattern topredict when an accident will happen or has happened.

5.1.5 Remote Sensing

Remote sensing such as analysis of imagery from satellites can providegeneral information about driving conditions, for example, weather.Resolution of imagery would typically be on the other of 1 square meteror more, so in most cases, you could not discern an individual vehicle.

5.2 Aerial Vehicles

A variety of flying vehicles can be used for aerial surveillance.Depending on the design criteria (the altitude of flight, the time inthe air, the area of coverage, the weather conditions anticipated),different aerial vehicles are better suited for different applications.Basic types of aerial vehicles include fixed-wing, traditionalhelicopters, multi-prop copters such as a hexi-copter, blimps ordirigibles; and variations or combinations of the above.

5.3 Deployment

5.3.1 Fixed Location

A fixed location implies that the aerial vehicle is normally housed onthe ground when not in use, in a single location that is more or lesscentral to area under surveillance. Size of the vehicle will depend onthe application.

5.3.2 Police or Other Moving Vehicle

Police or other emergency vehicles can be equipped with small ROVs orautonomous flying vehicles which can then be deployed when necessary.For real-time applications, such as deployment immediately after sensorsindicate that an accident has occurred, it will be necessary to have theflying device attached to the exterior of the vehicle or within acompartment that has a hatch that can open for deployment (Or simplykeep in the trunk of the vehicle for manual deployment. If fixed-wing,then some type of catapult may be attached to the flight vehicle; if arotary type flying vehicle, then some type of secure attachment that canrelease quickly and/or a spring or chemical propellant that can propelthe vehicle vertically.

5.3.3 Always in Flight

Air vehicle can be designed with electric motors powered by batterieswhich are in turn charged with solar panels. Alternatively, very lightweight slow moving fixed wing or blimp type vehicles can be up in theair for extended periods of time with minimal fuel.

5.3.4 Airport

In most cases using an airport for deployment would be reserved forlarge aerial vehicles that are subject to the same flight restrictionsas a piloted aircraft.

5.3.5 On-Vehicle

When wishing to capture information about an accident while it happensor shortly thereafter, in an embodiment, a flight vehicle is incommunication with sensors within the vehicle such as accelerometers.When either an impending accident or an accident in progress is detectedvia analysis of patterns, the flight vehicle is launched very rapidly inan attempt to have a vertical launch should the vehicle begin to rollover. The air vehicle could be a rotary type or a type of rocket with adeployable parachute. A rocket or similar device could be deployed muchlike a torpedo, from a tube, but vertically oriented.

5.4 Piloting System

5.4.1 Autonomous

Virtually any type of airframe can be made to take off or, land and flyautonomously. This would require location and altitude sensors as wellas some frame of reference, for example a digital map or a locationbeacon either at a fixed location or on a vehicle of interest.

5.4.2 ROV

If the application for surveillance is for accident site observation ornormally scheduled surveillance of a predefined area, then the flyingvehicle can be piloted remotely

5.4.3 Combination

In the above stated scenario for an ROV, a combination of remotepiloting and autonomous flight can be used. For example, take off andlanding can be remotely piloted, while in surveillance mode, the flightcould be autonomous.

5.4.4 with a Pilot

For high altitude flight or large vehicle flight, then, in anembodiment, an aerial vehicle will contain a human pilot.

5.5 Type of Aerial Vehicles

5.5.1 Quad or Multi-Copter

There is a variety of remotely operated or semi-autonomous vehicle whichachieve lift using one or more propellers. Configuration with 4 or 6blades usually mounted in the same plain and all oriented with thedirection of thrust perpendicular to the mounting plane. These copter ordrones as they are often called come in a variety of sizes from lessthan a kilogram in weight up to 20 kilograms or more

5.5.2 Fixed Wing

Of course fixed wing aircraft can be flown either piloted, autonomous orsemi-autonomously.

5.5.3 Blimp—for High Altitude Long Term Surveillance

Blimps have the advantage that they can stay in flight for extendedperiods as most of the energy is directed to moving the vehicle ratherthan keeping it aloft and the helium provides most of the lift.

5.5.4 Projectile with Parachute

For applications where close aerial surveillance is required at anaccident scene either while the accident is occurring or immediatelythereafter, a parachute mounted sensor suite which comprises a cameraand perhaps other sensing devices is contained in a cylindrical or otheraerodynamic container which in turn is attached to a chemical propellantor compressed gas engine or a kinetic energy device (for example aspring) capable of propelling the sensor suite and parachute at rapidspeed above the vehicle.

The motor or other propulsion device is actuated by a signal from thevehicle monitoring system (or potentially a remote systems) when itdetects an accident about to happen or that is in progress. Optionally,the vehicle monitoring system is equipped with a sensor or sensors (suchas a gyroscope) that can be used to determine if the vehicle is orientedwith the top of the car being up (within a threshold angle). If the topof the vehicle is not up and within the threshold angle of beingperpendicular to the vertical direction, the apparatus is notlaunched—to prohibit injury or damage to objects or people on theground.

FIG. 6 depicts a launch scenario in an embodiment of this invention thatutilizes a projectile with a parachute. The apparatus is housed in aweatherproof container with either a retractable hatch or cover that ispenetrable by the apparatus. The sensor suite is in standby mode 602 andin communication with a pattern detection module in the vehicle. If anaccident pattern is identified 604, the launch mechanism is checked tobe in a vertical position 606 and if so, the apparatus is launched 608,the hatch is either opened (prior to engine ignition) or penetrated whenthe apparatus lifts off. An example of a mechanism for launch would bemuch like a jack-in-the-box where a cover and latch hold into place theprojectile which is mounted on a spring. Once the latch is opened, theprojectile is free to exit and the spring force is released propellingthe projectile into the air.

Simultaneously with the launch (or previous to the launch) acommunication link is established between the launched sensor suite anda directional beacon on the vehicle 610.

At the apex of the flight of the apparatus (or at a specified time oraltitude above the vehicle, the parachute is deployed by various meansknown in the art. By using a direction antenna or other means, thevehicle is located and tracked.

The camera is mounted on a gimbal and servo motors keep the lensoriented towards the car. There may optionally be a servo to stabilizethe compass direction of the view of the camera, as the parachute andapparatus may be rotating.

Optionally, the apparatus is equipped with a propeller or propellant toprovide a horizontal and/or vertical forces to either prolong the lengthof time the apparatus can stay airborne or to be able to circle thevehicle for measurements at various altitudes above the vehicle orangles around the vehicle.

The camera may be equipped with a zoom lens to capture more or lessdetail of the accident scene.

Potential triggers (patterns) that would initiate a launch are the sameas described in the section on indications of an accident occurring orabout to happen.

FIG. 4 depicts a similar solution in the art (from European PatentApplication EP 0466499 A1). In EP 0466499 this is a battlefield aerialsurveillance device where a rocket is launched from a ground vehicle 49at time (A). At time (B) near the apex of the flight the aerodynamiccasing of the rocket is separated exposing the surveillance apparatus 9with a parachute 15 comprising a camera with a field of view 7 andconfigured with a device to prohibit rotation 29. The video istransmitted to a ground vehicle at time (E).

The present invention differs from EP0466499 in that the rocketdeployment is from the vehicle being surveyed and the deployment isinitiated based on sensor output and pattern recognition. In addition,the camera may be able to be directed and the parachute may besteerable. In addition, image software may be able to detect the vehicleof interest and zoom in on it.

5.5.5 Autonomous Air Vehicle Deployed from a Ground Vehicle

In an embodiment of this invention an autonomous air vehicle isdeployable from a ground vehicle. The air vehicle comprises acommunication module that is in wireless communication with on-boardsensors in the ground vehicle. If a pattern is detected by thesurveillance module in the ground vehicle that indicates that anaccident in progress or that an accident has happened, this in-turntriggers the launch of the autonomous air vehicle.

Commercial quadcopters are available in a small form factor, for exampleas shown in FIG. 5 (seehttp://www.alwaysinnovating.com/products/mecam.htm). A quadcopter ofthis size could be launched from a vehicle in a variety of ways:

-   -   A rigid quadcopter could be contained in a spherical container        housed in a vertical tube imbedded in the vehicle. A spring        loaded propulsion mechanism much like the mechanism used to        proper a ball bearing in a pin-ball machine could be held in        place by a latch. The latch could be triggered by the        recognition of an accident pattern.    -   Alternatively, the cross arms of the quadcopter could be folded        at a point where the two arms cross in the center such that two        adjacent motors are nearly touching one another on opposite        sides. The apparatus in the folded stated could be housed in a        bullet or rocket shaped container and launch much like the        parachute system of the previous section.        5.5.6 Combination of the Above

In an embodiment, a rocket is used to deploy payload of a sensor suiteattached to a fixed wing or rotary aircraft. An example of a vehiclethat may be suitable for this type of deployment is show, for example inU.S. Pat. No. 8,444,082 B1.

5.6 Fuel for Aerial Vehicle

Conventional methods for fueling an aerial vehicle include manypetroleum products including gasoline, aviation fuel, jet fuel, alcoholand others. In addition, smaller aircraft designed for short flight canuse light weight batteries and electric engines. For longer durationflights, for very light aircraft such as a blimp, solar panels or someform of photovoltaic can be affixed to surfaces of the craft to chargebatteries while in flight.

5.7 Sensors

There is a variety of sensors that can be used to determine both vehiclemovement and behavior and the conditions associated with the vehiclemovement and behavior. Various type of sensors may be used with aerialvehicles, at fixe ground locations or within vehicles.

Numerous sensors can be used in vehicle surveillance. A comprehensivesummary can be found in: A Summary of Vehicle Detection and SurveillanceTechnologies used in Intelligent Transportation Systems (seehttps://www.fhwa.dot.gov/ohim/tvtw/vdstits.pdf) although this text issomewhat dated now.

Examples of sensor that can be deployed from Aerial vehicles:

-   -   Camera—both natural color and infrared    -   LIDAR    -   GPS    -   Gyroscope    -   Digital Map

Examples of sensor that can be part of a vehicle or in a vehicle

-   -   Accelerometers    -   Temperature sensors    -   Forward facing camera    -   Backup camera    -   Air bag deployment    -   Gyroscope    -   GPS    -   Engine sensors    -   Tire pressure    -   Speedometer    -   Digital map    -   Seat belt or seat pressure sensor    -   ABS braking actuated

Examples of Road Side Sensors

-   -   Traffic Counts    -   Average Speed Limit    -   Weather and Road Condition Sensors    -   Vehicle Tracking

Part of a worldwide research effort in intelligent transportationsystems (ITS), there is a variety of methods using sensor networks ofvarious types to detect the movement of vehicles and track them. See forexample: A Study on Vehicle Detection and Tracking Using Wireless SensorNetworks 2010, by G. Padmavathi, D. Shanmugapriya, M. Kalaivani(http://www.scrip.org/journal/PaperInformation.aspx?PaperID=1385#.VK666XthQUU)

Examples of types of satellite imagery

-   -   Color image    -   Infrared image    -   Radar/Lidar        5.7.1 Location Determination Devices

5.7.1.1 Satellite Based

In virtually all of the surveillance methods, there is a need to knowwhere the surveillance vehicle is with respect to a vehicle or accidentlocation and/or with respect to the earth, for example, latitude andlongitude.

For a location relative to the earth, typically a GPS (Global PositionSatellite Receiver) is used. This type of device can also be used todetermine a low resolution altitude. As a GPS requires a line-of-siteview of 3 (or more) satellites to determine a position, sometimes is maybe necessary to augment a location determination with techniques knownin the art such as dead reckoning using a gyroscope, and/or a digitalcompass or other sensors. There are also other satellite locationsystems available from both Russia and the European Union.

In the case of a sensor suite deployed from a vehicle that willimminently be in an accident or was in an accident, it may only benecessary for the sensor suite to orient itself, relative to the planeof the earth and the location of the vehicle. This orientation can occurby using a beacon mounted in the vehicle.

5.7.1.2 Local Area Network Triangulation

Most portable electronic devices are equipped with some form of localarea networking, for example, Bluetooth Low Energy. As part of theprotocol for a communication standard such as this, there is a parameterthat is a measure of signal strength of the radio frequency signal thatis received by a receiver from a transmitter. It is well known in theart that by knowing the signal strength from three differenttransmitters that are geographic spaced, the relative location of thereceiver with respect to the three transmitters can be determined. Ofcourse there is a substantial amount of error in the signal strengthmeasurement so this method only provides an approximate relativelocation.

5.7.1.3 Radio Direction Finding

If a vehicle is equipped with a radio frequency transmitter and as partof a sensor suite that is deployed using a rocket or a aerial vehicledeployed from the vehicle, there is a directional antenna that receivesan indication of signal strength of the transmitted frequency from theradio transmitter, it is possible to determine the relative location ofthe sensor suite to the vehicle—so that video or other sensors can bedirected towards the vehicle.

5.7.1.4 Dead Reckoning

When a sensor suite is deployed from a moving vehicle, the vehicleand/or the sensor suite are equipped with sensors that can measure thevelocity and direction of motion. In an embodiment, at the time ofdeployment of the sensor suite, the direction of motion and the speed ofthe vehicle is known. The acceleration profile of the sensor suite basedon the propellant system used and the relative direction of deploymentwith respect to the vehicle motion is known. Assuming the that vehiclewill continue to travel in the same direction at the same speed, therelative position of the sensor suite with respect to the vehicle can becalculated through time. This will enable a gimbal mounted camera to becontinually pointed in the direction of the vehicle. Of course thisassumes that the vehicle continues to move in the same direction andspeed which would not necessary be the case if a collision occurs.Therefore, in an embodiment, the camera would initially point towardsthe vehicle, and would further register an image of the vehicle andtrack the vehicle using conventional image analysis software describedelsewhere in this document such that the video can be trained on thevehicle and not stay on the anticipated trajectory of the vehicle.

5.7.2 Altimeter

There are a variety of altimeters known in the art, which include onesbased on barometric pressure and/or a combination of barometric pressureand gps measurements and potential gyroscopic measurements. Altitude isimportant when dealing with position relative to the earth rather thanrelative to a moving vehicle.

5.7.3 Digital Map and Terrain Model

In scenarios where a pattern is used to identify when a vehicle isdriving erratically or when a vehicle is off the road, then once thelocation of a vehicle is identified, it must be compared with a digitalroad map in order to determine the above. Of course the accuracy of themeasurement of location of the vehicle and the accuracy of the digitalmap must be sufficient such that there is a high confidence of where thevehicle is relative to the road.

5.8 Patterns

A pattern is the term used to describe one or more time-series of sensorreadings that can be analyzed to:

-   -   Predict that an accident will happen    -   Determine that an accident has happened    -   Predict the extent of the damage and injury incurred during an        accident    -   Simply track a vehicle over time and its relationship to a road        network

The sensors involved can be associated with any or all of thesurveillance systems described above. Associated with each pattern is astatistical uncertainty in the prediction. Patterns may comprise a timeseries of a specific sensor measurements or may comprise a collection ofcalculated parameters inferred from a variety of sensor measurements.For example, acceleration could be measured directly by an accelerometeror inferred from location measurements over time from a GPS receiverand/or a combination of these two types of measurements could be used todetermine a mean acceleration for a given time interval by a weightedaverage of the two measurements, with more weight being attributed tothe measurement deemed the most accurate.

Patterns could also be analyzed in the frequency domain using Fourieranalysis

Patterns are determined by some form of multivariable analysis such asmachine learning where data is collected from sensors for many accidentswhere the extent of damage and severity of impact are known.

Raster image analysis can be considered another form of patternanalysis. In this case vehicles are identified and tracked.

Conventional analysis of measurements from several types of sensors thatmeasure different physical parameters over time may not be able toidentify complex patterns associated with an impending accident. Or itmight not be obvious how these measurements are related to a knownaccident hazard (for example ice on the road). Machine learningtechniques applied to historical data may identify complex patterns thatrelate sensor output overtime to accident potential without identifyingthe underlying cause or causes of the accident potential.

Determination of patterns that can be used to predict or detectaccidents and the results of accidents are described in: U.S. patentapplication titled “SYSTEM AND METHOD FOR USE OF PATTERN RECOGNITION INASSESSING OR MONITORING VEHICLE STATUS OR OPERATOR DRIVING BEHAVIOR”,application Ser. No. 13/679,722, filed Nov. 16, 2012; which claims thebenefit of priority to U.S. Provisional Patent Application No.61/578,511, filed Dec. 21, 2011; PCT/US12/71487 titled “SYSTEMS ANDMETHODS FOR ASSESSING OR MONITORING VEHICLE STATUS OR OPERATOR STATUS”filed 21 Dec. 2012; and Ser. No. 14/317,624 titled “System and methodfor Determining Of Vehicle Accident information” file on 27 Jun. 2014;each of which the above applications are herein incorporated byreference.

Below are examples of physical events that can be used to anticipate animpending accident. These indicators can be measured in a variety ofways with a variety of sensors and one or more of these measurements canbe incorporated into patterns. These are just examples and as statedabove, patterns may be determined using machine learning that cannot becorrelated with a single physical event, but never-the-less have astrong correlation with an impending accident and an accident inprogress. The indicators below and other can be used to, for example, toinitiate an airborne launch of a sensor suite. Examples of patternsand/or physical events are:

-   -   Rapid deceleration above a specific threshold that would        indicate emergency braking. One method of detection of rapid        deceleration would be to monitoring vehicle onboard        accelerometers and gyros. Airbag deployment    -   Rapid change in direction that would indicated spinning on ice        or locked brakes [again, augmented by accelerometer and gyro        sensing]    -   Extremely close proximity to other vehicles as detected by video        camera or other proximity sensor such as sonar, radar or        infrared

Patterns may be expressed as polynomial equation; they may be athreshold constant or upper and lower range for a specific sensor; theymay be based on frequency and/or amplitude analysis of a single type ormultiple types of sensors or they could be a statistical mean value forone or more sensor outputs or environmental factors. Patterns willchange over time as more data is added, more sophisticated analysis isperformed or more sensor types are available for on-board measurement.Patterns for one type of vehicle may be entirely different than foranother type of vehicle. This may be due to different sensor suitesbeing available or different physical attributes of the vehicle.

5.8.1 Image Analysis Software to Detect Ground Vehicles

In order to detect vehicles from raster images, one method is to usevehicle recognition software. Patterns in an image that are indicativeof a vehicle. There are several methods for analyzing both video, stilland infrared imagery to detect vehicles. One example of a method forrecognizing vehicles in a image is Real-time People and VehicleDetection from UAV Imagery by Ga̧szczak, A, et al (seehttp://breckon.eu/toby/publications/papers/gaszczak11uavpeople.pdf). Bytracking the location of an identified vehicle through an image overtime, the acceleration and velocity of the vehicle can also bedetermined. If the image is orthorectified to align with a digital roadnetwork, then the location of a vehicle with respect to the road networkcan be determined.

5.8.2 Patterns from Vehicle Sensors

New data is collected from vehicle on-board sensors and from externalfeeds such as sensor suites that are part of the road network system orfor example from weather satellites. At given time intervals the datafor the last time period is stored and analyzed and the older data isthrown out (provided no patterns of interest were detected).Alternatively, the data is stored in a memory stack of a set size wherenew data is added to top of the stack and the oldest data (at the bottomof the stack) is thrown out. At intervals which could correspond to thesample interval or multiples of the sampling rate, an accident patternor impending accident pattern is looked for. If a patterns is detected,indicating an accident or impending accident has occurred or will occur,then the sampling rate may be increased to acquire more data per timeperiod, and/or other sensor data, previously not being recorded, may berecorded.

The end of the accident event, in an embodiment, is defined when thevehicle is stationary. Once the accident is over, the stored data isanalyzed to detect damage and injury patterns. If accident and/or injurypatterns are detected, then the location and estimated damage and injuryassociated with these patterns is recorded and transmitted to pertinentindividuals or computer servers.

If the severity of injury anticipated by the analysis is sufficient,then an ambulance and/or paramedic is contacted (provided communicationis available). If a tow is needed, then a tow vehicle is called,provided the local information for such services is available. Aninsurance adjuster is contacted. Parts and repair services are queriedto check availability. Depending on the configuration, information aboutthe accident is displayed on an infotainment screen in the vehicle or onan authorized portable device.

The raw data and/or the sensor analysis is transferred to a server viathe communication network (wireless, or otherwise) for inclusion intothe central database and for future determination of accident, damageand injury patterns.

FIG. 3 illustrates and embodiment using a monitoring system within thevehicle. Real-time time series data is acquired from many sensorson-board the vehicle 302 and transmitted to an Accident PredictionModule 310. In addition, the Accident Prediction Module 310 receivesexternal information from other surveillance systems 308 by wirelesscommunication. At intervals, the Accident Prediction Module 310 performsanalysis comparing the sensor data feeds 302, 308 to accident patternsacquired from a historical database 304. If an accident pattern ismatched to the sensor feeds, this triggers recording of detailedinformation and a search for damage and injury patterns within the data.If a damage or injury pattern is detected, then analysis is performedconcerning the extent of damage or injury and the location of damage orinjury and this information along with the underlying data istransmitted to interested parties.

It may be desirable to limit the data/parameters that are utilized andmake some simplifying assumptions.

Accident detection patterns could be inferred simply by knowing theweight of the car and inferring a maximum acceleration or change inmomentum that would indicate an accident occurred. Damage and Injurypatterns are approximated by relating specific accident descriptions toranges of acceleration or momentum and the direction of impact. Onceaccidents are categorized as to the location and severity of damage interms of anticipated range of acceleration that occurred during anevent, then a cross correlation between repairs and injury treatmentsrequired for a given vehicle type can be made with the each range ofacceleration.

Examples of patterns to record are:

-   -   Number of Occupants of the vehicle    -   Impact zone    -   Roll or skid characteristics    -   Deceleration    -   Movement of occupants    -   Distortion of passenger compartment    -   Breaking of glass    -   Thermal (indication of fire)    -   Rear facing camera—following car too close    -   Front facing camera—too close to forward car; cars serving in        from other lanes    -   Rapid steering changes    -   Brakes Locking        5.8.3 Pre-Accident Aerial Surveillance

It is desirable to predict when an accident will occur prior to ithappening so the accident can be observed while in progress oralternatively to initiate some action that would avoid the accident.Some examples of patterns that might indicate an accident about tohappen would be:

-   -   A vehicle exceeding the speed limit over a certain threshold    -   Erratic driving behavior    -   Vehicles without a safe distance between    -   If questionable behavior found:        -   Record video; record speed/acceleration profile; record lane            changes        -   Monitor for a prescribed time or until an accident occurs        -   OCR the license plate; send warning message for continued            bad driving; citation if bad behavior does not cease.    -   If no questionable behavior lock onto another vehicle        5.8.4 Aerial Monitoring of Distance Between Vehicles

Using image analysis techniques described previously, it is possible toidentify specific vehicles in images and in successive images, identifythose same vehicles as they move through an area. By knowing therelative time when the images were acquired and the location of thevehicles in the image, parameters or patterns can be determined.

If the relative distance between two vehicles as determined in eithermacro or local aerial monitoring becomes less than a thresholdvalue—indicating that collision is about to occur—several actions couldhappen:

-   -   Information acquired about the vehicles of interest for the time        leading up to the time period in question is set aside and        stored.    -   Velocity and Accelerations are calculated    -   Event surveillance assets are communicated with and deployed    -   Sensor systems within the cars themselves are contacted via        wireless communication and instructed to record information at a        rapid rate.    -   Pre-accident patterns from the vehicle/s are compared with        patterns either from macro aerial or local aerial surveillance        systems to verify the analysis    -   If the aerial surveillance is capable, acquire the license        number of the vehicles involved    -   If one or more of the vehicles is moving out of the surveillance        area, alert adjacent surveillance areas to be on the look-out.    -   When in communication with vehicle equipped with deployable        surveillance systems, signal that one should be deployed        5.8.5 On-Vehicle Monitoring of Adjacent Vehicles

A variety of methods exist in the art to determine the distance betweenone vehicle and adjacent ones. There are a variety of sensors that canbe used to detect an adjacent vehicle. Video cameras for example couldbe used in conjunction with vehicle detection software to know when anadjacent vehicle is too close. Adjacent vehicles will reflect light andother forms of electromagnetic radiation such as infrared, and/or may beequipped with an active transponder which transmits a signal which canbe located and identified.

5.8.6 On-Vehicle Accident Detection

The ultimate goal of using in-vehicle accident detection would be toanticipate an accident before it happens to enable deployment ofemergency services and also to assess the severity of the impendingaccident in real-time. Rapid assessment immediately after an accident isalso the goal—should there be no way to detect the accident beforehand.

Modern vehicles are generally equipped with a variety of sensors thatmeasure physical parameters associated with the moving vehicle. Thesesensors can be a part of the vehicle or within the vehicle, for exampleas part of a mobile device.

Vehicle behavior can be inferred based on patterns exhibited in thesensor data overtime—either from observations of a single type of sensoror a sensor suite, for example a gyroscope and also a 3 componentaccelerometer. Rapid changes in the orientation of the vehicle may beexhibited by changes in the values measured by a gyroscope and/oraccelerometers. It is intuitively known, for example, if a car isspinning on wet pavement or on ice, that there is a strong likelihoodthat the vehicle will sustain damage and/or passengers will be injured.However, this likelihood can be quantified by tracking patterns in thesensor output leading up to previous accidents with known damage andinjury—performing statistical analysis on those patterns. It may bedetermined that if a vehicle spins 360 degrees within 3 seconds, whenthe initial speed was 90 kph, that there is a 90 percent probabilitythat the vehicle will flip over. If we take into account the type ofvehicle, it may be apparent that a vehicle with a high center of gravitywill have a higher probability of rolling over than a vehicle with a lowcenter of gravity. It may further be found that if a vehicle flips withthe initial speed that there is a 50% probably of severe injury to apassenger in the front seat.

5.8.7 Remote Accident Detection—Aerial

Patterns observable from aerial surveillance may indicate:

-   -   A burning vehicle (infrared signature)    -   Vehicle off-road or eschew on the lane (based on image        recognition of vehicle location when compared to a digital road        map)    -   Overturned vehicle (as indicated by changes in the image        recognition profile of the vehicle)        5.9 On-Vehicle Accident Detection Hardware

Hardware for an on-board accident detection and analysis systemcomprises the following components:

-   -   a processor which monitors and analyses onboard sensors used to        detect vehicle activity and driver behavior;    -   an on-board database comprising:        -   vehicle specific information;        -   patterns, for the individual vehicle type, used to analyze            sensor data to detect accidents and to assess resulting            injury and damage and useful to predict driver behavior and            driver/insurance risk;        -   driver information;        -   emergency contact information;    -   one or more of several data transmission components which can        include both short range wireless (for example Bluetooth), long        range wireless transmission (for example mobile service) and        wired data communication component—which can communicate with        external servers to transmit both raw sensor data and        damage/injury estimation and to provide software and database        updates to the vehicle on-board system.    -   a remote central server in communication with multiple vehicle        systems comprising:        -   one or more computers;        -   a comprehensive central database located on one or more            servers comprising:            -   historical information from several sources            -   raw sensor data or indices derived from the raw sensor                data from individual vehicles.        -   patterns for all vehicle types and areas        -   geographic Information        -   spatial, temporal, and severity Information pertaining to            historical accident incidents        -   metadata            5.10 Procedures Concerning Response to Patterns            5.10.1 Response after Accident Detected

If an accident pattern is detected by any surveillance method, then inan embodiment, the following scenario would occur:

-   -   send wireless transmission to a surveillance dispatch    -   look for drones, fixed cameras or moving camera that are in the        vicinity of the detected accident    -   dispatch and/or point surveillance device at the accident    -   once an accident is identified, circle from several angles for a        3-D view    -   transmit Data to an Accident Investigator and/or emergency        services        5.10.2 Response for Cars Equipped with Own Drone, Once Accident        is Detected

Once a pattern from an incident detection system is identified thatwould indicate with a high degree of certainty that an accident is aboutto happen, the system will initiate the following sequence:

-   -   1) launch drone or rocket        -   a) if the vehicle is equipped with a spring loaded hatch,            open it        -   b) check the orientation of the car to make sure that the            launch will be relatively vertical—based on vehicle sensor            input such as magnetometers or accelerometers.    -   2) simultaneous to launch or prior to the launch, establish        wireless communication with the vehicle including one or more        location beacons    -   3) once the drone or rocket reaches its apex, use a gyro in the        device to stabilize orientation (down facing down)    -   4) use a gimbal mount and servo motors to orient a video camera        or other sensor in the direction of the vehicle    -   5) if equipped, use vehicle recognition to zoom the camera or        other sensors in such that the vehicle or vehicle that are in        the accident fill the field of view (in other embodiment both a        near and fare range image are taken.    -   6) do several passes of vehicle at various altitudes with        respect to the vehicle altitude and several view angles        5.11 Historical Analysis of Collected Data        5.11.1 Patterns for Sensor Records and Accident Reports

The following tasks comprise one method to determine accident patternsinitially based on accident reports:

-   -   Develop transfer functions between observations in historical        databases built from accident reports to on-board sensor        measurements that are indicative of the observed damage. For        example, an accident impact could be inferred when a rapid        deceleration is detected either by accelerometer measurements or        change in speed measurements. Location, and relative speed of an        impact can be inferred based on 3 component acceleration.        Alternatively, a side impact can be inferred when a side airbag        is deployed.    -   Test the transfer function by predicting vehicle damage and        resulting cost based on sensor data after an accident. Confirm        the prediction based on conventional accident and insurance        adjustor reports.    -   Refine the transfer functions as necessary to increase        statistical reliability.    -   Gradually incorporate sensor measurements and create a more        granular predictive models based solely on sensor measurements        (without inference from historical data not from sensors). In        the initial database collisions may be classified based on        relative speed of impact, for example. With more accurate speed        data from sensors and vehicle weights, the classification could        be changed to an impact momentum in N/m² using finer ranges for        classification rather than simply an approximate relative speed        of collision.        5.11.2 Comparison of Traffic, Weather and Sensor Data and the        Likelihood of an Accident

After performing analysis of many sensors reading from many vehicle andcorrelating this information with weather information and road conditioninformation, patterns may emerge that can be used to identify area andtimeframes where accident are very likely to occur. These predictionscan then be used to deploy aerial surveillance system for increasedsurveillance in accident prone zone or at accident prone timeframes.

5.12 Combining Datasets Measuring the Same Parameter from DifferentTypes of Sensors or Sensors with Differing Resolution

Ideally, it would be easier to identify specific patterns indicative ofan imminent accident, an accident event or damage and injury related toan accident if the database comprised identical measurements, forexample if the sampling rate and resolution and accuracy of anaccelerometer was always the same. In practice, this would never happenas sensor technology continues to advance. Therefor predictions based onlow resolution, accuracy or a slow sampling rate must have an indicationthat the prediction is less certain than a prediction based on betterquality information. As new, better quality information is stored in thedatabase, older, poorer quality data should be removed from the databaseand patterns adjusted accordingly.

Raw data may need to be parameterized in such a way as they can be usedinto a numeric model. An example of parameterization would be tocharacterize incidents into a grouping. For example, it may be desirableto collectively refer to impact force based on accelerometer readings inranges in units of meters/second² rather than actual recorded values oras a mean over a time frame.

5.13 Database Maintenance

Database maintenance comprises removing older or poorer quality data,continually updating the patterns as newer and better information comeson line. In addition, as the database increases in size, patterns can bebroken into smaller subdivisions, for example, an accident pattern couldbe vehicle type specific as to vehicle class specific.

5.14 Database Content

In addition to raw sensor output, patterns, currency of data and theresolution and accuracy of the data must be stored. Other pertinentinformation is:

-   -   Location of fixed sensors systems including range of operation    -   Range of fixed based aerial vehicles and sweep are; length of        deployment; weather extremes that operation can occur.    -   Standard sensor suites in a stock vehicles—including access        protocol and frequency, procession and accuracy of measurements    -   Video resolution and color perception and/or frequency range        5.15 Example Embodiments

In an embodiment, a vehicle accident surveillance network comprises atleast one of:

-   -   a) one or more surveillance systems which in turn comprises:        -   i) a sensor suite configured to observe ground based            vehicles;        -   ii) a pattern recognition module configured to interpret the            sensor suite readings as vehicle movements, locations,            pending accidents, and accident incidents and to identify            specific vehicles; and        -   iii) a wireless transceiver configured to transmit and            receive the identity and location of specific vehicles that            had pattern identified, to surveillance systems;    -   b) one or more deployable aerial surveillance systems        comprising:        -   i) an airframe configured to launch from one of a ground            based vehicle, and fixed base station, and a larger            airframe, wherein a launch is triggered by detection of a            pattern indicative of an accident occurring or about to            occur as detected by one or more of the surveillance            systems;        -   ii) a second wireless transceiver configured to receive the            identity and location of the vehicle or vehicles which            correspond to identified patterns from one or more            surveillance systems;        -   iii) a directional sensor suite configured to be directed            towards the identified vehicle or vehicles after deployment            of the aerial surveillance system;        -   iv) an aerial surveillance module configured to:            -   1. receive the location of the vehicle or vehicles                identified by one or more surveillance systems;            -   2. launch the one or more deployable aerial surveillance                systems;            -   3. after being launched, determine the relative location                of the identified vehicle or vehicles, and one or more                of: approach the identified vehicle, circle the accident                scene at a predetermined circumference and altitude, and                point the directional sensors towards the vehicle or                vehicles and record the sensor data.

In an embodiment, a deployable aerial surveillance system is configuredwith a receiver that can identify a location beacon (for exampleattached to a vehicle) and track the location beacon.

In an embodiment, deployable aerial surveillance systems are deployed byan operator when the system is provided with one or more of:

-   -   c) coordinates of a vehicle to be surveyed;    -   d) a trajectory of a vehicle to be surveyed; and    -   e) specification of a location beacon that resides in the        vehicle to be surveyed that can be tracked by the system.

Vehicle accident surveillance network can be one or more of:

-   -   f) an airborne surveillance system;    -   g) a ground based vehicle equipped with a surveillance system;        and    -   h) a ground based stationary surveillance system.

In an embodiment, airborne surveillance systems are configured:

-   -   i) with image detection sensors that observe the earth below in        a plurality of spectral bands; and    -   j) the pattern detection module is configured to detect vehicles        using image analysis techniques.

Surveillance systems, in an embodiment comprises a memory cacheconfigured to store sensor data from sensors for a predetermined timeprior to the present time and further configured to save this data upondetection of a pattern and continue to save incoming sensor data for apredetermined time after the pattern is detected.

An embodiment of a vehicle accident surveillance system installed in aground vehicle comprises:

-   -   k) an on-vehicle sensor suite configured to observe location and        motions of the ground vehicle;    -   l) a pattern recognition module configured to interpret the        sensor suite readings as pending accidents, and accident        incidents; and    -   m) a deployable aerial surveillance system comprising:        -   i) an airframe configured to launch from the vehicle when            triggered by detection of a pattern indicative of an            accident occurring or about to occur;        -   ii) a directional sensor suite configured to be directed            towards the vehicle after deployment of the aerial            surveillance system;        -   iii) an aerial surveillance module configured to:            -   (1) launch the deployable aerial surveillance system;            -   (2) after being launched, determine the relative                location of the vehicle, and one or more of: approach                the vehicle, circle the vehicle at a predetermined                altitude and circumference from the vehicle and point                the directional sensors towards the vehicle and record                the sensor data.

A vehicle accident surveillance system installed in a ground vehicle canoptionally be configure with a directional sensor suite that containsone or more cameras.

A transceiver in an accident surveillance system is optionallyconfigured to:

-   -   n) communicate with other accident surveillance systems; and    -   o) launch a deployable aerial surveillance system to observe the        ground vehicle, the vehicle containing the requesting        surveillance system or other ground vehicles.

In an embodiment, a vehicle accident surveillance system installed in aground vehicle comprises:

-   -   p) an on-vehicle sensor suite configured to observe location and        motions of the ground vehicle;    -   q) a pattern recognition module configured to interpret the        sensor suite readings as pending accidents, and accident        incidents; and    -   r) a wireless transmitter configured to transmit a request to        nearby surveillance systems to deploy and monitor the ground        vehicle should the pattern recognition module detect a pattern        indicative of a potential accident or accident.

Embodiments of the present disclosure may be conveniently implementedusing one or more conventional general purpose or specialized digitalcomputers or microprocessors programmed according to the teachings ofthe present disclosure, or a portable device (e.g., a smartphone, tabletcomputer, computer or other device), equipped with a data collection andassessment environment, including one or more data collection devices(e.g., accelerometers, GPS) or where the portable device are connectedto the data collection devices that are remote to the portable device,that are connected via wired or wireless means. Appropriate softwarecoding can readily be prepared by skilled programmers based on theteachings of the present disclosure, as will be apparent to thoseskilled in the software art. In some embodiments, a computer programproduct may be included, which is a non-transitory storage medium(media) having instructions stored thereon/in which can be used toprogram a computer to perform any of the processes of the presentinvention. The storage medium can include, but is not limited to, anytype of disk including floppy disks, optical discs, DVD, CD-ROMs,micro-drive, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs,DRAMs, VRAMS, flash memory devices, magnetic or optical cards,nano-systems (including molecular memory ICs), or any type of media ordevice suitable for storing instructions and/or data.

6 REMARKS

The foregoing description of the present invention has been provided forthe purposes of illustration and description. It is not intended to beexhaustive or to limit the invention to the precise forms disclosed. Theembodiments were chosen and described in order to best explain theprinciples of the invention and its practical application, therebyenabling others skilled in the art to understand the invention forvarious embodiments and with various modifications that are suited tothe particular use contemplated. It is intended that the scope of theinvention be defined by the following claims and their equivalence.

What is claimed:
 1. A vehicle accident surveillance network, comprising: (a) at least one first airborne surveillance system comprising— an airframe configured for autonomous powered flight over an identified surveillance area carrying (i) a sensor suite configured to observe ground-based vehicles traversing said surveillance area using image-based sensing and (ii) a first system wireless transceiver configured to transmit and receive identification and location of specific ground-based vehicles traversing said surveillance area; at least one pattern recognition module configured to use image analysis to identify patterns from sensor suite readings corresponding to ground-based vehicle movements representative of imminent accidents and ground-based vehicle accident incidents having occurred, the pattern recognition module disposed on the airframe or on a ground based system communicating with the airframe; a memory cache configured to store sensor data from sensors for a predetermined time prior to a present time and further configured to save the stored data upon detection of a pattern and continue to save incoming sensor data for a predetermined time after the pattern is detected; and a first system aerial surveillance module configured for passive mode surveillance including to generate an accident alert upon identification by the pattern recognition module of an imminent accident or accident incident having occurred, said alert including vehicle identification and location and being transmittable by said first system wireless transceiver; and (b) plural deployable second aerial surveillance systems, each said second deployable aerial surveillance system comprising— an airframe configured for powered flight carrying a directional sensor suite including an imaging device configured to be directed towards identified ground-based vehicle or vehicles after deployment, and a second system wireless transceiver disposed on the airframe configured to receive accident alerts generated and transmitted by said at least one first airborne surveillance system; and a second system aerial surveillance module communicating with the second wireless transceiver and directional sensor suite, said second system aerial surveillance module configured (i) for active mode surveillance including to deploy at least one said deployable second aerial surveillance system to the alert location, and direct the directional sensor suite towards the ground-based vehicle or vehicles and record sensor data through said directional sensor suite including images and additional specific sensor data correlated to a specific pattern identification, and (ii) with a receiver that can identify a location beacon and track the location beacon.
 2. The vehicle accident surveillance network of claim 1, wherein said first system aerial surveillance module is further configured to collect vehicle information based on said sensor suite readings, said vehicle information including identification of vehicles entering the identified surveillance area, tracking of identified vehicles within said surveillance area, and speed and acceleration of identified vehicles through said surveillance area, and to store or transmit said vehicle information through the first system wireless transceiver.
 3. The vehicle accident surveillance network of claim 1, wherein deploying said at least one said deployable second aerial surveillance system comprises launching and controlling at least one said deployable second aerial surveillance system to approach an identified vehicle or circle an identified accident scene at a predetermined circumference and altitude.
 4. The vehicle accident surveillance network of claim 1, wherein said deployable second aerial surveillance systems are further configured to be deployed by an operator when provided with one or more of: coordinates of the ground-based vehicle to be surveyed; a trajectory of the ground-based vehicle to be surveyed; and specification of a location beacon that resides in the ground-based vehicle to be surveyed that can be tracked by the system.
 5. The vehicle accident surveillance network of claim 1, wherein said airframes comprise one or more of: a fixed wing aircraft; a rotary aircraft with one or more rotors; and a lighter than air craft.
 6. The vehicle accident surveillance network of claim 1, wherein said at least one said sensor suite is configured with image detection sensors that observe the earth below in a plurality of spectral bands.
 7. The vehicle accident surveillance network of claim 1, wherein one or both of the first and second system wireless transceivers are configured to transmit sensor data and analysis to interested parties comprising one or more of: first responders; insurance adjusters; and ground-based vehicle owners.
 8. The vehicle accident surveillance network of claim 1, wherein said first airborne surveillance systems are additionally configured to monitor driving conditions for ground-based vehicles.
 9. The vehicle accident surveillance network of claim 1, wherein said plural deployable second aerial surveillance systems comprise one or more of autonomously powered airframes, remote operated vehicles (ROV), and airframes selectively employing autonomous power and ROV control.
 10. The vehicle accident surveillance network of claim 1, further comprising at least one ground-based surveillance system, comprising: a ground-based sensor suite configured to observe ground-based vehicles traversing a surveillance area surrounding the ground-based sensor suite using image-based sensing; and a ground-based wireless transceiver configured to transmit and receive identification and location of specific ground-based vehicles traversing said surveillance area.
 11. The vehicle accident surveillance network of claim 10, wherein said at least one pattern recognition module comprises a pattern recognition module communicating with said at least one first airborne surveillance system and said at least one ground-based surveillance system via a communications network.
 12. The vehicle accident surveillance network of claim 1, wherein said at least one pattern recognition module comprises plural pattern recognition modules disposed on said at least one first airborne surveillance system airframe and said second aerial surveillance system airframes.
 13. A vehicle accident surveillance network, comprising: (a) at least one first airborne surveillance system comprising— an airframe configured for autonomous powered flight over an identified surveillance area carrying (i) a sensor suite configured to observe ground-based vehicles traversing said surveillance area using image-based sensing, (ii) a first system wireless transceiver configured to transmit and receive identification and location of specific ground-based vehicles traversing said surveillance area, and (iii) a first computing device comprising a processor and memory including a memory cache configured to store sensor data from sensors for a predetermined time prior to a present time and further configured to save the stored data upon detection of a pattern and continue to save incoming sensor data for a predetermined time after the pattern is detected, said first computing device configured to contain and execute: a pattern recognition module configured to use image analysis to identify patterns from sensor suite readings corresponding to ground-based vehicle movements representative of imminent accidents and ground-based vehicle accident incidents having occurred; and a first system aerial surveillance module communicating with said sensor suite to receive readings therefrom and configured to configured for passive mode surveillance including to collect vehicle information based on said sensor suite readings including identification and of vehicles entering the identified surveillance area, and speed and acceleration of identified vehicles through said surveillance area, and to generate an accident alert transmittable by said first system wireless transceiver including vehicle identification and location upon identification by the pattern recognition module of an imminent accident or accident incident having occurred; and (b) plural deployable second aerial surveillance systems, each said second deployable aerial surveillance system comprising— an airframe configured for powered flight carrying (i) a directional sensor suite including an imaging device configured to be directed towards identified ground-based vehicle or vehicles after deployment, (ii) a second system wireless transceiver disposed on the airframe configured to receive accident alerts generated and transmitted by said at least one first airborne surveillance system and to identify a location beacon and track the location beacon, and (iii) a second computing device comprising a processor and memory, said second computing device is configured to contain and execute: a pattern recognition module configured to use image analysis to identify patterns from sensor suite readings corresponding to ground-based vehicle movements representative of imminent accidents and ground-based vehicle accident incidents having occurred; and a second system aerial surveillance module communicating with the second wireless transceiver and directional sensor suite, said second system aerial surveillance module configured for active mode surveillance including to deploy at least one said deployable second aerial surveillance system to the alert location, and direct the directional sensor suite towards the ground-based vehicle or vehicles, control the deployed second aerial surveillance system to approach an identified vehicle or circle an identified accident scene at a predetermined circumference and altitude, and record sensor data through said directional sensor suite including specific sensor data representing at least the identified vehicle or accident scene correlated to a specific pattern identification.
 14. A vehicle accident surveillance network, comprising: (a) at least one first airborne surveillance system comprising— an airframe configured for autonomous powered flight over an identified surveillance area carrying (i) a sensor suite configured to observe ground-based vehicles traversing said surveillance area using image-based sensing and (ii) a first system wireless transceiver configured to transmit and receive identification and location of specific ground-based vehicles traversing said surveillance area; at least one pattern recognition module configured to use image analysis to identify patterns from sensor suite readings corresponding to ground-based vehicle movements representative of imminent accidents and ground-based vehicle accident incidents having occurred, the pattern recognition module disposed on the airframe or on a ground based system communicating with the airframe; a memory cache configured to store sensor data from sensors for a predetermined time prior to a present time and further configured to save the stored data upon detection of a pattern and continue to save incoming sensor data for a predetermined time after the pattern is detected; and a first system aerial surveillance module configured for passive mode surveillance including to generate an accident alert upon identification by the pattern recognition module of an imminent accident or accident incident having occurred, said alert including vehicle identification and location and being transmittable by said first system wireless transceiver; and (b) plural second surveillance systems, each said second surveillance system comprising— a second system wireless transceiver configured to receive accident alerts generated and transmitted by said at least one first airborne surveillance system; and a second system surveillance module communicating with the second wireless transceiver and directional sensor suite, said second system surveillance module configured for active mode surveillance at the alert location, including directing the directional sensor suite towards the ground-based vehicle or vehicles and recording sensor data through said directional sensor suite including specific sensor data correlated to a specific pattern identification for the alert.
 15. The vehicle accident surveillance network of claim 14, wherein said plural second surveillance systems comprise at least one deployable aerial surveillance system comprising an airframe configured for powered flight carrying said directional sensor suite, said second system wireless transceiver and said second system surveillance module.
 16. The vehicle accident surveillance network of claim 15, wherein said plural second surveillance systems comprise a network of ground-based surveillance systems, each ground-based surveillance system comprising a ground-based sensor suite configured to observe ground-based vehicles traversing a surveillance area surrounding the ground-based sensor suite using image-based sensing; a ground-based wireless transceiver configured to transmit and receive identification; and location of specific ground-based vehicles traversing said surveillance area; and one said second system surveillance module.
 17. The vehicle accident surveillance network of claim 16, wherein said at least one first airborne surveillance system airframe further carries a computing device comprising a processor and memory, said computing device configured to contain and execute one said pattern recognition module and the first system aerial surveillance module.
 18. The vehicle accident surveillance network of claim 17, wherein at least one said deployable aerial surveillance system airframe further carries a computing device comprising a processor and memory, said computing device configured to contain and execute one said pattern recognition module and the second system surveillance module. 